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Record W2170336710 · doi:10.1007/s10865-011-9380-2

Does action planning moderate the intention-habit interaction in the exercise domain? A three-way interaction analysis investigation

2011· article· en· W2170336710 on OpenAlexaff
Gert‐Jan de Bruijn, Ryan E. Rhodes, Liesbeth van Osch

Bibliographic record

VenueJournal of Behavioral Medicine · 2011
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHabitPsychologyAction (physics)Health psychologySocial psychologyDevelopmental psychologyMedicinePublic health

Abstract

fetched live from OpenAlex

Both habit strength and action planning have been found to moderate the intention-exercise behaviour relationship, but no research exists that has investigated how habit strength and action planning simultaneously influence this relationship. The present study was designed to explore this issue in a prospective sample of undergraduate students (N = 415): action planning, habit strength, intention, attitudes, subjective norms and perceived behavioural control were assessed at baseline and exercise behaviour was assessed 2 weeks later. Both habit strength and action planning moderated the intention-exercise relationship, with stronger relationship at higher levels of planning or habit strength. Decomposing a significant action planning × habit strength × intention interaction showed that the strength of the intention-exercise relationship progressed linearly through levels of action planning and habit strength. These novel results show that action planning strengthens the intention-habit strength interaction in the exercise domain: exercise interventions should therefore focus on simultaneously bolstering action planning and habit strength.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.226
GPT teacher head0.456
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations72
Published2011
Admission routes1
Has abstractyes

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